As EHR systems are widely deployed, we have access to large amounts of patient data for research purposes. Addressing the challenges of data representation, Common Data Model, defined by Observational Medical Outcomes Partnership (OMOP), allows for the development of tools and analyses that work on multiple datasets of Electronic Health Record (EHR) data or administrative claims data. It allows for phenotype modelling across institutions and has been adopted by NIH All-Of-Us research project. Our goal was to replicate the current technology stack of OMOP-based tools for visualization and phenotype modelling LHNCBC, namely, the data profiling tool Achilles and data visualization tool called Atlas. As sample EHR data, we used NIH Biomedical Translational Research Information System (BTRIS) data from deceased subjects obtained via BTRIS limited data set application (with given diagnoses, treatments and means). We created an ETL (Extract, Transform, Load) pipeline that transforms EHR data in phases into the OMOP CDM. Our phased approach can be used on other input data and extends existing OMOP tools for ETL, taking advantage of OMOP vocabularies to translate source data into OMOP targeted terminologies (e.g., RxNorm, LOINC and SNOMED CT). In conclusion, fully implemented OMOP based technology stack allows us to pilot clinical analyses using OHDSI statistical packages and tools. In addition to BTRIS data, we hope to re-use this experience with two additional EHR datasets (e.g., MIMIC-III ICU data).